Atlas of functional connectivity relationships across rare and common genetic variants, traits, and psychiatric conditions

Authors: Clara A. Moreau, …, Pierre Bellec, and Sebastien Jacquemont

April 2021

Interactive brainmaps (mean beta values per region (MIST64)

https://github.com/claramoreau9/NeuropsychiatricCNVs_Connectivity/
In [1]:
import pandas as pd
import nibabel as nib
import seaborn as sbn
import pathlib as pal
from nilearn import plotting #Abraham, A. et al. Machine learning for neuroimaging with scikit-learn. Front. Neuroinform. 8, 14 (2014).
from nilearn import input_data as nii
from nilearn import plotting as nlp 
from matplotlib import pyplot as plt
In [ ]:
mist_64_p = '/Users/Clara/Desktop/MIST/Parcellations/MIST_64.nii.gz'
labels_p = '/Users/Clara/Desktop/MIST/Parcel_Information/MIST_64.csv'
mist64_i = nib.load(mist_64_p)
mist64 = mist64_i.get_data()
labels = pd.read_csv(labels_p, delimiter=';')
mask = mist64_i.get_data().astype(bool)
In [3]:
root_p = pal.Path('/Users/Clara/Desktop/Paper_CrossCNVs/Figure_1/Betamaps_AH/dataset_30_03_21/').resolve()
mask_i = nib.Nifti1Image(mask, affine=mist64_i.affine, header=mist64_i.header)
masker = nii.NiftiMasker(mask_img=mask_i, standardize=False)
masker.fit()
atlas_vec = masker.fit_transform(mist64_i).squeeze() - 1

1q21.1

In [4]:
COND_p = root_p / 'cc_DEL1q21_1_mc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="DEL 1q21.2 GSA",vmax=1, threshold=0.1, cmap=plt.cm.seismic)
Out[4]:
In [5]:
COND_p = root_p / 'cc_DUP1q21_1_mc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="DUP 1q21.2 GSA",vmax=1, threshold=0.1, cmap=plt.cm.seismic)
Out[5]:
In [6]:
COND_p = root_p / 'cc_DEL1q21_1_nomc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="DEL 1q21.2 no GSA",vmax=1, threshold=0.1, cmap=plt.cm.seismic)
Out[6]:
In [7]:
COND_p = root_p / 'cc_DUP1q21_1_nomc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="DUP 1q21.2 no GSA",vmax=1, threshold=0.3, cmap=plt.cm.seismic)
Out[7]:

13q12.12

In [8]:
COND_p = root_p / 'cc_DEL13q12_12_mc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="DEL 13q12.12 GSA",vmax=1, threshold=0.1, cmap=plt.cm.seismic)
Out[8]:
In [9]:
COND_p = root_p / 'cc_DUP13q12_12_mc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="DUP 13q12.12 GSA",vmax=1, threshold=0.1, cmap=plt.cm.seismic)
Out[9]:
In [10]:
COND_p = root_p / 'cc_DEL13q12_12_nomc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="DEL 13q12.12 no GSA",vmax=1, threshold=0.1, cmap=plt.cm.seismic)
Out[10]:
In [11]:
COND_p = root_p / 'cc_DUP13q12_12_nomc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="DUP 13q12.12 no GSA",vmax=1, threshold=0.4, cmap=plt.cm.seismic)
Out[11]:

15q11.2

In [12]:
COND_p = root_p / 'cc_DEL15q11_2_mc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="DEL 15q11.2 GSA",vmax=1, threshold=0.05, cmap=plt.cm.seismic)
Out[12]:
In [13]:
COND_p = root_p / 'cc_DUP15q11_2_mc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="DUP 15q11.2 GSA",vmax=1, threshold=0.05, cmap=plt.cm.seismic)

/Users/Clara/anaconda3/lib/python3.6/site-packages/nilearn/reporting/html_document.py:60: UserWarning: It seems you have created more than 10 nilearn views. As each view uses dozens of megabytes of RAM, you might want to delete some of them.
  MAX_IMG_VIEWS_BEFORE_WARNING))
Out[13]:
In [14]:
COND_p = root_p / 'cc_DEL15q11_2_nomc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="DEL 15q11.2 no GSA",vmax=1, threshold=0.05, cmap=plt.cm.seismic)

/Users/Clara/anaconda3/lib/python3.6/site-packages/nilearn/reporting/html_document.py:60: UserWarning: It seems you have created more than 10 nilearn views. As each view uses dozens of megabytes of RAM, you might want to delete some of them.
  MAX_IMG_VIEWS_BEFORE_WARNING))
Out[14]:
In [15]:
COND_p = root_p / 'cc_DUP15q11_2_nomc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="DUP 15q11.2 no GSA",vmax=1, threshold=0.05, cmap=plt.cm.seismic)

/Users/Clara/anaconda3/lib/python3.6/site-packages/nilearn/reporting/html_document.py:60: UserWarning: It seems you have created more than 10 nilearn views. As each view uses dozens of megabytes of RAM, you might want to delete some of them.
  MAX_IMG_VIEWS_BEFORE_WARNING))
Out[15]:

16p11.2

In [16]:
COND_p = root_p / 'cc_DEL16p11_2_mc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="DEL 16p11.2 GSA",vmax=1, threshold=0.1, cmap=plt.cm.seismic)

/Users/Clara/anaconda3/lib/python3.6/site-packages/nilearn/reporting/html_document.py:60: UserWarning: It seems you have created more than 10 nilearn views. As each view uses dozens of megabytes of RAM, you might want to delete some of them.
  MAX_IMG_VIEWS_BEFORE_WARNING))
Out[16]:
In [17]:
COND_p = root_p / 'cc_DUP16p11_2_mc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="DUP 16p11.2 GSA",vmax=1, threshold=0.1, cmap=plt.cm.seismic)

/Users/Clara/anaconda3/lib/python3.6/site-packages/nilearn/reporting/html_document.py:60: UserWarning: It seems you have created more than 10 nilearn views. As each view uses dozens of megabytes of RAM, you might want to delete some of them.
  MAX_IMG_VIEWS_BEFORE_WARNING))
Out[17]:
In [18]:
COND_p = root_p / 'cc_DEL16p11_2_nomc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="DEL 16p11.2 no GSA",vmax=1, threshold=0.1, cmap=plt.cm.seismic)

/Users/Clara/anaconda3/lib/python3.6/site-packages/nilearn/reporting/html_document.py:60: UserWarning: It seems you have created more than 10 nilearn views. As each view uses dozens of megabytes of RAM, you might want to delete some of them.
  MAX_IMG_VIEWS_BEFORE_WARNING))
Out[18]:
In [19]:
COND_p = root_p / 'cc_DUP16p11_2_nomc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="DUP 16p11.2 no GSA",vmax=1, threshold=0.1, cmap=plt.cm.seismic)

/Users/Clara/anaconda3/lib/python3.6/site-packages/nilearn/reporting/html_document.py:60: UserWarning: It seems you have created more than 10 nilearn views. As each view uses dozens of megabytes of RAM, you might want to delete some of them.
  MAX_IMG_VIEWS_BEFORE_WARNING))
Out[19]:

16p13.11 DUP

In [20]:
COND_p = root_p / 'cc_DUP16p13_11_mc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="DUP 16p13.11 GSA",vmax=1, threshold=0.05, cmap=plt.cm.seismic)

/Users/Clara/anaconda3/lib/python3.6/site-packages/nilearn/reporting/html_document.py:60: UserWarning: It seems you have created more than 10 nilearn views. As each view uses dozens of megabytes of RAM, you might want to delete some of them.
  MAX_IMG_VIEWS_BEFORE_WARNING))
Out[20]:
In [21]:
COND_p = root_p / 'cc_DUP16p13_11_nomc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="DUP 16p13.11 no GSA",vmax=1, threshold=0.1, cmap=plt.cm.seismic)

/Users/Clara/anaconda3/lib/python3.6/site-packages/nilearn/reporting/html_document.py:60: UserWarning: It seems you have created more than 10 nilearn views. As each view uses dozens of megabytes of RAM, you might want to delete some of them.
  MAX_IMG_VIEWS_BEFORE_WARNING))
Out[21]:

17p12 DEL

In [22]:
COND_p = root_p / 'cc_DEL17p12_mc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="DEL 17p12 GSA",vmax=1, threshold=0.1, cmap=plt.cm.seismic)

/Users/Clara/anaconda3/lib/python3.6/site-packages/nilearn/reporting/html_document.py:60: UserWarning: It seems you have created more than 10 nilearn views. As each view uses dozens of megabytes of RAM, you might want to delete some of them.
  MAX_IMG_VIEWS_BEFORE_WARNING))
Out[22]:
In [23]:
COND_p = root_p / 'cc_DEL17p12_nomc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="DEL 17p12 no GSA",vmax=1, threshold=0.1, cmap=plt.cm.seismic)

/Users/Clara/anaconda3/lib/python3.6/site-packages/nilearn/reporting/html_document.py:60: UserWarning: It seems you have created more than 10 nilearn views. As each view uses dozens of megabytes of RAM, you might want to delete some of them.
  MAX_IMG_VIEWS_BEFORE_WARNING))
Out[23]:

22q11.2

In [24]:
COND_p = root_p / 'cc_DEL22q11_2_mc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="DEL 22q11.2 GSA",vmax=1, threshold=0.1, cmap=plt.cm.seismic)

/Users/Clara/anaconda3/lib/python3.6/site-packages/nilearn/reporting/html_document.py:60: UserWarning: It seems you have created more than 10 nilearn views. As each view uses dozens of megabytes of RAM, you might want to delete some of them.
  MAX_IMG_VIEWS_BEFORE_WARNING))
Out[24]:
In [25]:
COND_p = root_p / 'cc_DUP22q11_2_mc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="DUP 22q11.2 GSA",vmax=1, threshold=0.1, cmap=plt.cm.seismic)

/Users/Clara/anaconda3/lib/python3.6/site-packages/nilearn/reporting/html_document.py:60: UserWarning: It seems you have created more than 10 nilearn views. As each view uses dozens of megabytes of RAM, you might want to delete some of them.
  MAX_IMG_VIEWS_BEFORE_WARNING))
Out[25]:
In [26]:
COND_p = root_p / 'cc_DEL22q11_2_nomc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="DEL 22q11.2 no GSA",vmax=1, threshold=0.1, cmap=plt.cm.seismic)

/Users/Clara/anaconda3/lib/python3.6/site-packages/nilearn/reporting/html_document.py:60: UserWarning: It seems you have created more than 10 nilearn views. As each view uses dozens of megabytes of RAM, you might want to delete some of them.
  MAX_IMG_VIEWS_BEFORE_WARNING))
Out[26]:
In [27]:
COND_p = root_p / 'cc_DUP22q11_2_nomc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="DUP 22q11.2 no GSA",vmax=1, threshold=0.1, cmap=plt.cm.seismic)

/Users/Clara/anaconda3/lib/python3.6/site-packages/nilearn/reporting/html_document.py:60: UserWarning: It seems you have created more than 10 nilearn views. As each view uses dozens of megabytes of RAM, you might want to delete some of them.
  MAX_IMG_VIEWS_BEFORE_WARNING))
Out[27]:

Idiopathic psychiatric conditions

In [28]:
COND_p = root_p / 'cc_ASD_mc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="Autism GSA",vmax=1, threshold=0.1, cmap=plt.cm.seismic)

/Users/Clara/anaconda3/lib/python3.6/site-packages/nilearn/reporting/html_document.py:60: UserWarning: It seems you have created more than 10 nilearn views. As each view uses dozens of megabytes of RAM, you might want to delete some of them.
  MAX_IMG_VIEWS_BEFORE_WARNING))
Out[28]:
In [29]:
COND_p = root_p / 'cc_ASD_nomc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="Autism no GSA",vmax=1, threshold=0.1, cmap=plt.cm.seismic)

/Users/Clara/anaconda3/lib/python3.6/site-packages/nilearn/reporting/html_document.py:60: UserWarning: It seems you have created more than 10 nilearn views. As each view uses dozens of megabytes of RAM, you might want to delete some of them.
  MAX_IMG_VIEWS_BEFORE_WARNING))
Out[29]:
In [30]:
COND_p = root_p / 'cc_SZ_mc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="Schizophrenia GSA",vmax=1, threshold=0.1, cmap=plt.cm.seismic)

/Users/Clara/anaconda3/lib/python3.6/site-packages/nilearn/reporting/html_document.py:60: UserWarning: It seems you have created more than 10 nilearn views. As each view uses dozens of megabytes of RAM, you might want to delete some of them.
  MAX_IMG_VIEWS_BEFORE_WARNING))
Out[30]:
In [31]:
COND_p = root_p / 'cc_SZ_nomc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="Schizophrenia no GSA",vmax=1, threshold=0.2, cmap=plt.cm.seismic)

/Users/Clara/anaconda3/lib/python3.6/site-packages/nilearn/reporting/html_document.py:60: UserWarning: It seems you have created more than 10 nilearn views. As each view uses dozens of megabytes of RAM, you might want to delete some of them.
  MAX_IMG_VIEWS_BEFORE_WARNING))
Out[31]:
In [32]:
COND_p = root_p / 'cc_BIP_mc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="Bipolar GSA",vmax=1, threshold=0.1, cmap=plt.cm.seismic)

/Users/Clara/anaconda3/lib/python3.6/site-packages/nilearn/reporting/html_document.py:60: UserWarning: It seems you have created more than 10 nilearn views. As each view uses dozens of megabytes of RAM, you might want to delete some of them.
  MAX_IMG_VIEWS_BEFORE_WARNING))
Out[32]:
In [33]:
COND_p = root_p / 'cc_BIP_nomc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="Bipolar no GSA",vmax=1, threshold=0.2, cmap=plt.cm.seismic)

/Users/Clara/anaconda3/lib/python3.6/site-packages/nilearn/reporting/html_document.py:60: UserWarning: It seems you have created more than 10 nilearn views. As each view uses dozens of megabytes of RAM, you might want to delete some of them.
  MAX_IMG_VIEWS_BEFORE_WARNING))
Out[33]:

Polygenic scores GSA

In [34]:
COND_p = root_p / 'cont_Stand_PRS_SCZ_mc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="PGS-SZ GSA",vmax=0.1, threshold=0.005, cmap=plt.cm.seismic)

/Users/Clara/anaconda3/lib/python3.6/site-packages/nilearn/reporting/html_document.py:60: UserWarning: It seems you have created more than 10 nilearn views. As each view uses dozens of megabytes of RAM, you might want to delete some of them.
  MAX_IMG_VIEWS_BEFORE_WARNING))
Out[34]:
In [35]:
COND_p = root_p / 'cont_Stand_PRS_ASD_mc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="PGS-ASD GSA",vmax=0.1, threshold=0.002, cmap=plt.cm.seismic)

/Users/Clara/anaconda3/lib/python3.6/site-packages/nilearn/reporting/html_document.py:60: UserWarning: It seems you have created more than 10 nilearn views. As each view uses dozens of megabytes of RAM, you might want to delete some of them.
  MAX_IMG_VIEWS_BEFORE_WARNING))
Out[35]:
In [36]:
COND_p = root_p / 'cont_Stand_PRS_MDD_mc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="PGS-MDD GSA",vmax=0.1, threshold=0.002, cmap=plt.cm.seismic)

/Users/Clara/anaconda3/lib/python3.6/site-packages/nilearn/reporting/html_document.py:60: UserWarning: It seems you have created more than 10 nilearn views. As each view uses dozens of megabytes of RAM, you might want to delete some of them.
  MAX_IMG_VIEWS_BEFORE_WARNING))
Out[36]:
In [37]:
COND_p = root_p / 'cont_Stand_PRS_newCDG2_ukbb_mc.tsv'
COND=pd.read_csv(COND_p, sep='\t', header=None)
COND=COND.mean(axis=1)

COND_perc_disc = COND
COND_brain_vec = COND_perc_disc[atlas_vec]
COND_brain_img = masker.inverse_transform(COND_brain_vec)

nlp.view_img(COND_brain_img, title="PGS-CrossD GSA",vmax=0.1, threshold=0.002, cmap=plt.cm.seismic)

/Users/Clara/anaconda3/lib/python3.6/site-packages/nilearn/reporting/html_document.py:60: UserWarning: It seems you have created more than 10 nilearn views. As each view uses dozens of megabytes of RAM, you might want to delete some of them.
  MAX_IMG_VIEWS_BEFORE_WARNING))
Out[37]: